Abstract
The preservation of many music works produced in the past entails their digitalization and consequent accessibility in an easy-to-manage digital format. Carrying this task manually is very time consuming and error prone. While optical music recognition systems usually perform well on printed scores, the processing of handwritten musical scores by computers remain far from ideal. One of the fundamental stages to carry out this task is the staff line detection. In this paper a new method for the automatic detection of music staff lines based on a connected path approach is presented. Lines affected by curvature, discontinuities, and inclination are robustly detected. Experimental results show that the proposed technique consistently outperforms well-established algorithms.

Abstract
Breast cancer conservative treatment (BCCT) is considered the gold standard of breast cancer treatment. However, aesthetic results are heterogeneous and difficult to evaluate in a standardised way. The limited reproducibility of subjective aesthetic evaluation in BCCT forced the research on objective methods. A recent computer system was developed to objectively and automatically evaluate the aesthetic result of BCCT In this system, the detection of the breast contour on the digital photograph of the patient is necessary to extract the features subsequently used in the evaluation process. In this paper we extend an algorithm based on the shortest path on a graph to detect automatically the breast contour. The advantage of graph algorithms is that they are guaranteed to find the global optimum of the problem; the difficulty is that they make it hard to enforce shape constraints. We define and compare different techniques to introduce the a priory knowledge of the mammary contour. Experimental results show that the proposed techniques consistently outperform the base method.

Abstract
Many music works produced in the last century still exist only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a digital format easy-to-manage which encourages browsing, retrieval, search and analysis while providing a generalized access to the digital material. The manual process to carry out this task is very time consuming and error prone. Automatic optical music recognition (OMR) has emerged as a partial solution to this problem. However, the full potential of this process only reveals itself when integrated in a system that provides seamless access to browsing, retrieval, search and analysis. We address this demand by proposing a modular, flexible and scalable framework that fully integrates the abovementioned functionalities. A web based system to carry out the automatic recognition process, allowing the creation and management of a music corpus, while providing generalized access to it, is a unique and innovative approach to the problem. A prototype has been implemented and is being used as a test platform for OMR algorithms.

Abstract
Many music works produced in the past are currently available only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a machine-readable format, which encourages browsing, retrieval, search and analysis while providing a generalized access to the digital material. Carrying this task manually is very time consuming and error prone. While optical music recognition (OMR) systems usually perform well on printed scores, the processing of handwritten music by computers remains below the expectations. One of the fundamental stages to carry out this task is the detection and subsequent removal of staff lines. In this paper we integrate a general-purpose, knowledge-free method for the automatic detection of staff lines based on stable paths, into a recently developed staff line removal toolkit. Lines affected by curvature, discontinuities, and inclination are robustly detected. We have also developed a staff removal algorithm adapting an existing line removal approach to use the stable path algorithm at the detection stage, Experimental results show that the proposed technique outperforms well-established algorithms. The developed algorithm will now be integrated in a web based system providing seamless access to browsing, retrieval, search and analysis of submitted scores.

Abstract
Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes where the order relation is ignored. This paper introduces a new machine learning paradigm intended for multi-class classification problems where the classes are ordered. The theoretical development of this paradigm is carried out under the key idea that the random variable class associated with a given query should follow a unimodal distribution. In this context, two approaches are considered: a parametric, where the random variable class is assumed to follow a specific discrete distribution; a nonparametric, where the random variable class is assumed to be distribution-free. In either case, the unimodal model can be implemented in practice by means of feedforward neural networks and support vector machines, for instance. Nevertheless, our main focus is on feedforward neural networks. We also introduce a new coefficient, r(int), to measure the performance of ordinal data classifiers. An experimental study with artificial and real datasets is presented in order to illustrate the performances of both parametric and nonparametric approaches and compare them with the performances of other methods. The superiority of the parametric approach is suggested, namely when flexible discrete distributions, a new concept introduced here, are considered.

Abstract